Jonathan Klein, Martin Laurenzis, Dominik L. Michels and Matthias B. Hullin

In: Proceedings of the British Machine Vision Conference (BMVC 2018), Northumbria University, Newcastle, UK, September 3-6, 2018

Abstract:

The computational sensing community has recently seen a surge of works on imaging beyond the direct line of sight. However, most of the reported results rely on drastically different measurement setups and algorithms, and are therefore hard to impossible to compare quantitatively. In this paper, we focus on an important class of approaches, namely those that aim to reconstruct scene properties from time-resolved optical impulse responses. We introduce a collection of reference data and quality metrics that are tailored to the most common use cases, and we define reconstruction challenges that we hope will aid the development and assessment of future methods.

Paper

Supplementary Material

Poster

BibTeX Citation

@inproceedings{KleinEtAl-NLOSChallenge-BMVC2018,
    author = {Jonathan Klein and Martin Laurenzis and Dominik L. Michels and Matthias B. Hullin},
    title = {A Quantitative Platform for Non-Line-of-Sight Imaging Problems},
    booktitle = {British Machine Vision Conference 2018, {BMVC} 2018,
        Northumbria University, Newcastle, UK, September 3-6, 2018},
    pages = {104},
    year = {2018},
}